IBM Analytics Engine vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #8, while Apache is ranked #5. IBM holds a 3.1% mindshare in H, compared to Apache’s 6.6% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 86% of Apache users who would recommend it.

IBM Analytics Engine

Read 1 IBM Analytics Engine review

193 Views
185 Comparison Views

100% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

637 Views
554 Comparison Views

86% willing to recommend

IBM Analytics Engine

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between IBM Analytics Engine and Spark SQL based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

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Buyer's Guide

Hadoop

January 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

IBM Analytics Engine

Ranking in Hadoop

8th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of IBM Analytics Engine is 3.1%, up from 1.8% compared to the previous year. The mindshare of Spark SQL is 6.6%, down from 10.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Spark SQL	6.6%
IBM Analytics Engine	3.1%
Other	90.3%

Hadoop

Featured Reviews

Saket Pandey

Product Manager at a hospitality company with 51-200 employees

Good solution for small and medium-sized businesses and highly stable

I would advise instead of only going through other reviews; it would be great if you could schedule a talk with the IBM team that would be helping you implement this solution. They would deep dive into the process and protocols you are currently set up in, and then they will provide you an optimal solution and optimal price. So I believe talking with the support team was really amazing. They even helped us in some other parts as well. It is a good solution for small and medium-sized businesses. Overall, I would rate the solution an eight out of ten because of the support team. They were able to resolve issues, which helped us deploy higher-grade solutions correctly and quickly. We were able to ensure that our processes were working correctly, and we saved about 15-16% of a week's time by using this solution. In terms of return on investment, we saved about $7,000 a month.

Read full review

Kemal Duman

Team Lead, Data Engineering at Nesine.com

Data pipelines have run faster and support flexible batch and streaming transformations

We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The best part was that we could make minor changes in the way we were bifurcating the data, even at a very small scale. The accuracy of conversion was also very high."

"The stability was fine. It behaved as expected."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"I find the Thrift connection valuable."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"It is a stable solution."

"The speed of getting data."

"This solution is useful to leverage within a distributed ecosystem."

More Spark SQL pros

Cons

"One area for improvement would be the initial setup stage, which took longer than expected."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"In the next release, maybe the visualization of some command-line features could be added."

"Anything to improve the GUI would be helpful."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"SparkUI could have more advanced versions of the performance and the queries and all."

"It would be useful if Spark SQL integrated with some data visualization tools."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

More Spark SQL cons

Pricing and Cost Advice

Information not available

"There is no license or subscription for this solution."

"We use the open-source version, so we do not have direct support from Apache."

"The solution is open-sourced and free."

"The solution is bundled with Palantir Foundry at no extra charge."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

16%

University

16%

Retailer

13%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	6
Large Enterprise	4

Comparisons

HPE Data Fabric vs IBM Analytics Engine

Compared 67% of the time

Amazon EMR vs IBM Analytics Engine

Compared 33% of the time

More IBM Analytics Engine Competitors

Apache Spark vs Spark SQL

Compared 30% of the time

HPE Data Fabric vs Spark SQL

Compared 16% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 16% of the time

SAP HANA vs Spark SQL

Compared 14% of the time

Amazon EMR vs Spark SQL

Compared 12% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Hadoop

January 2026

Download IBM Analytics Engine product report

Buyer's Guide

Spark SQL

January 2026

Download Spark SQL product report

Overview

IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of compute notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms.

IBM

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

Information Not Available

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: January 2026.

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